This survey study analyzed the existing literature on the relationship between energy consumption and economic growth in the six Gulf Cooperation Council (GCC) countries (Saudi Arabia, United Arab Emirates, Bahrain, Qatar, Oman and Kuwait). This study identified 59 articles published in 18 journals covering the period 2006–2019. The articles were grouped into two categories: the first category included studies analyzing the energy–growth relationship at the individual country level while the second category included studies analyzing the relationship at a multi-country level. The result of this study revealed that 18% of the observations supported the growth hypothesis, 26% supported the conservation hypothesis, 43% supported the feedback hypothesis and 13% supported the neutral hypothesis. As our analysis found a dominant support for the growth and feedback hypotheses, this implies that the focus of energy policies in GCC countries has been on the supply and the uninterrupted availability for the expansion and growth of their industrial and developmental activities. However, for a sustainable development and growth of the GCC economies and meeting the environmental challenges, there is an urgent need for the expansion of renewable energy technologies in the energy supply mix of GCC countries.

Purpose Operations managers are subjected to various cognitive biases, which may lead them to make less optimal decisions as suggested by the normative models. In their seminal work, Tversky and Kahneman introduced three heuristics based on which people make decisions: representativeness, availability, and anchoring. This paper aims to investigate the six cognitive biases resulting from the use of the representativeness heuristic, namely, insensitivity to prior probability of outcomes, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity, and misconception of regression. Specifically, the paper examines how cognitive reflection and training affect these six cognitive biases in the operations management context. Methods For each cognitive bias, a scenario related to operations management was developed. The participants of the experimental study are asked to select among three responses, where one response is correct and the other two are biased. A total of 315 students from the University of North Texas participated in this study and 302 valid responses were used in the analysis. Results The results show that in all six scenarios, >50% of the respondents make biased decisions. However, using simple training, the bias is significantly reduced. Regarding the relationship between cognitive biases and cognitive reflection, the results partially support the hypothesis that people with high cognitive reflection ability tend to make less biased decisions. Regarding the effect of training on making biased decisions, the results show that making people aware of the existence of cognitive biases helps them partially to avoid making biased decisions. Conclusion Overall, our study demonstrates the value of training in helping operations managers make less biased decisions. Our discussion section offers some related guidelines for creating a professional environment where the effect of the representativeness heuristic is minimized.

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This article aims to review, analyze, and classify the published research applications of the Data Envelopment Analysis (DEA) window analysis technique. The number of filtered articles included in the study is 109, retrieved from 79 journals in the web of science (WoS) database during the period 1996–2019. The papers are classified into 15 application areas: energy and environment, transportation, banking, tourism, manufacturing, healthcare, power, agriculture, education, finance, petroleum, sport, communication, water, and miscellaneous. Moreover, we present descriptive statistics related to the growth of publications over time, the journals publishing the articles, keyword terms used, length of articles, and authorship analysis (including institutional and country affiliations). To the best of the authors knowledge, this is the first survey reviewing the literature of the DEA window analysis applications in the 15 areas mentioned in the paper.

This study provides a systematic analysis of research on the electricity sector in Gulf Cooperation Council (GCC) countries in the period 1983–2018. GCC countries have experienced tremendous economic growth in the past few decades. This was accompanied by a corresponding increase in electricity consumption. Therefore, a thorough review is needed to understand the research conducted on the electricity sector in GCC countries. This study reviewed articles published in five well-known energy journals: Applied Energy, Energy, Energy Economics, Energy Policy, and Renewable and Sustainable Energy Reviews. The articles were classified into seven categories based on the analysis tools implemented in the papers: 1. Simulation tools, 2. Scenarios tools, 3. Equilibrium tools, 4. Top-down tools, 5. Bottom-up tools, 6. Operations optimization tools, and 7. Investment optimization tools. This study also provides an overview of the research, including the increase in publications over time, an authorship analysis, a keywords analysis, and an analysis of the length of the publications.

The aim of this research was to explore the relationship between the push, pull, anti-push, and anti-pull factors vs. early retirement intention among Saudi medical staff, and to investigate whether there are gender differences in the early retirement intention. To this end, we designed a correlational and cross-sectional study, for which data were collected through an online survey. A total of 680 responses were gathered, of which 221 valid responses constituted the final sample for the analysis. Logistics regression was used to test the hypotheses of the study. The results showed that approximately 58% of the respondents indicated early retirement intention. The significant factors in predicting this intention were the pull, anti-push, and anti-pull factors, whereas the push factors were found to be insignificant. Moreover, female medical staff tend to retire earlier than males. Strategies recommended to delay retirement are providing flexible work hours, working shorter shifts or on a part-time basis, offering programs for professional development, and according more recognition.

A common technique for eliciting subjective probabilities is to provide a set of exclusive and exhaustive events and ask the assessor to estimate the probabilities of such events. However, such subjective probabilities estimations are usually subjected to a bias known as the partition dependence bias. This study aims to investigate the effect of state space partitioning and the level of knowledge on subjective probability estimations. The state space is partitioned into full, collapsed, and pruned trees, while the knowledge is manipulated into low and high levels. A scenario called “Best Bank Award” was developed and a 2 × 3 experimental design was employed to explore the effect of the level of knowledge and the partitioning of the state space on the subjective probability. A total of 627 professionals participated in the study and 543 valid responses were used for analysis. The results of two-way ANOVA with the Tukey HSD test for post hoc analysis indicate a mean probability of 24.2% for the full tree, which is significantly lower than those of the collapsed (35.7%) as well as pruned (36.3%) trees. Moreover, there is significant difference in the mean probabilities between the low (38.1%) and high (24.9%) knowledge levels. The results support the hypotheses that the partitioning of the state space as well as the level of knowledge affects subjective probability estimation. The study demonstrates that regardless of the level of knowledge, the partition dependence bias is robust. However, the subjective probability accuracy improves with more knowledge.